Non-intrusive load monitoring via online compression and reconstruction

被引：0
作者：
Zhou, Qiu-Zhan [1 ]
Ji, Ze-Yu [1 ]
Wang, Cong [1 ]
Rong, Jing [1 ]
机构：
[1] College of Communication Engineering, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2024年 / 54卷 / 06期
关键词：
communication and information system; compressed sensing; distributed deployment; long short-term memory; non-intrusive load monitoring;
D O I：
10.13229/j.cnki.jdxbgxb.20220927
中图分类号：
学科分类号：
摘要：
Accurate monitoring of power loads in home scenarios often relies on complex algorithmic models that are difficult to deploy in edge devices. At the same time,massive power data poses a huge challenge to communication of grid. In response to the above issues,this paper proposes a distributed nonintrusive load monitoring method. This method calculates and identifies the operating state of the load by the LSTM load monitoring algorithm based on the attention mechanism, and distributes the load monitoring task in the cloud and the edge with the help of cloud-edge collaboration technology to solve the problem of insufficient edge computing power. Aiming at the high network bandwidth requirements brought about by cloud-side communication,the compressed sensing method based on K-SVD double sparse online dictionary is used to compress and reconstruct the load signal,which effectively alleviates the shortage of communication resources. Comparing the performance of the monitoring algorithm under different load scenarios,the results show that the load monitoring algorithm in this paper can maintain an accuracy rate of more than 95%. Experiments are conducted to verify the effectiveness of the compressed sensing method on the compression of load signals,and determine the maximum compression ratio of load data without distortion. © 2024 Editorial Board of Jilin University. All rights reserved.
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页码：1796 / 1806
页数：10
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